Sensing reality and communicating bits: a dangerous liaison

Gastpar, Michael; Vetterli, Martin; Dragotti, Pier Luigi

doi:10.1109/msp.2006.1657818

Cited by 76 publications

(58 citation statements)

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“…In graph and network theory, researchers are looking for measures of the information encoded in node interactions in order to quantify the complexity of the network [5]. In communication theory, sensor networks usually generate strongly-correlated data [6]; a haphazard design might not account for these interdependencies and, undesirably, will process and transmit redundant information across the network, degrading the efficiency of the system. gate.…”

Section: Introductionmentioning

confidence: 99%

Understanding Interdependency Through Complex Information Sharing

Rosas

Ntranos

Ellison

et al. 2016

Entropy

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Abstract:The interactions between three or more random variables are often nontrivial, poorly understood and, yet, are paramount for future advances in fields such as network information theory, neuroscience and genetics. In this work, we analyze these interactions as different modes of information sharing. Towards this end, and in contrast to most of the literature that focuses on analyzing the mutual information, we introduce an axiomatic framework for decomposing the joint entropy that characterizes the various ways in which random variables can share information. Our framework distinguishes between interdependencies where the information is shared redundantly and synergistic interdependencies where the sharing structure exists in the whole, but not between the parts. The key contribution of our approach is to focus on symmetric properties of this sharing, which do not depend on a specific point of view for differentiating roles between its components. We show that our axioms determine unique formulas for all of the terms of the proposed decomposition for systems of three variables in several cases of interest. Moreover, we show how these results can be applied to several network information theory problems, providing a more intuitive understanding of their fundamental limits.

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Section: Introductionmentioning

confidence: 99%

Understanding Interdependency Through Complex Information Sharing

Rosas

Ntranos

Ellison

et al. 2016

Entropy

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“…Indeed, if each photon is thought of as a "noisy sensor," our hypothetical system bears similarities to several macroscopic frameworks studied in the signal processing literature-i.e. the "refining sensor network" of [23], or the "stochastic pooling network" of [19]. …”

Section: P (N|t=tm)mentioning

confidence: 99%

Signal acquisition via polarization modulation in single photon sources

McDonnell

Flitney

2009

Phys. Rev. E

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A simple model system is introduced for demonstrating how a single photon source might be used to transduce classical analog information. The theoretical scheme results in measurements of analog source samples that are (i) quantized in the sense of analog-to-digital conversion and (ii) corrupted by random noise that is solely due to the quantum uncertainty in detecting the polarization state of each photon. This noise is unavoidable if more than one bit per sample is to be transmitted, and we show how it may be exploited in a manner inspired by suprathreshold stochastic resonance. The system is analyzed information theoretically, as it can be modeled as a noisy optical communication channel, although unlike classical Poisson channels, the detector's photon statistics are binomial. Previous results on binomial channels are adapted to demonstrate numerically that the classical information capacity, and thus the accuracy of the transduction, increases logarithmically with the square root of the number of photons, N . Although the capacity is shown to be reduced when an additional detector nonideality is present, the logarithmic increase with N remains.Extensive efforts to establish practical quantum computers incorporating quantum communication and/or cryptography have led towards the development of reliable Single Photon Sources (SPS) [1,2]. While much research in this area is targeted towards quantum information processing, and the concept of qubits [3], the impending availability of a reliable SPS may also have broader application.Here we introduce and analyze a paradigm of quantum photonic information processing that falls beyond the scope of classical Poisson light [4,5,6,7], sub-Poisson squeezed light [8] and the usual focus of quantum information theory [3]. We consider a model for the transduction of classical information via a quantum optical channel and a classical detector.In our model, an information source directly modulates a SPS that emits photons with equal energy at a known constant rate. Unlike the Poisson or sub-Poisson statistics of a classical optical source, this results in deterministic photon release counts. Significant recent experimental progress [2,9] indicates that an SPS may soon offer the possibility of modulation of the information source onto quantum properties of individual photons, namely the polarization angle. Thus the focus of this paper is on quantifying the potential benefits from exploiting such a controllable SPS polarization angle in sensing applications.We model the information source to be sensed (the 'signal') using the standard information theoretic approach, i.e. we treat the signal as a discrete random variable. While our system results in a quantum information channel, it is capable of transmitting classical information, as considered, e.g., in [10,11]. This is similar to the scenario in [10,12] where two photons are considered in the context of the Holevo bound [3]. However it differs in that we specify that an arbitrary (but constant) number of photons, N , are use...

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“…An interesting counterexample can be found in [12] for lossless transmission of correlated sources through an interfering (nonorthogonal) multiple access channel. In this case, separating source from channel coding is suboptimal (see also [20]). …”

Section: On the Optimal Cost-distortion Tradeoffmentioning

confidence: 99%

Distributed compression-estimation using wireless sensor networks

Jian

Ribeiro

Luo

et al. 2006

IEEE Signal Process. Mag.

285

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Public Reporting Burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. ABSTRACTIn this paper we consider deterministic parameter estimation problems. We study the intertwining of quantization and estimation in general and shows particular results in i) low SNR situations where the noise standard deviation is in the order of the parameter's dynamic range; and ii) universal estimation when the sensor data and noise model are unknown. The goal is to understand how the signal processing capability of a WSN scales up with its size, and to develop robust distributed signal processing algorithms and protocols with low bandwidth requirement and optimal performance. We show that for universal estimation in low signal to noise ratio (SNR), the universal distributed estimators not only exist but achieve performance close to that of estimators based on the original (un-quantized) observations. We also generalize these results a Bayesian estimation framework with a particular application to state estimation of dynamic stochastic processes.

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Sensing reality and communicating bits: a dangerous liaison

Cited by 76 publications

References 50 publications

Understanding Interdependency Through Complex Information Sharing

Understanding Interdependency Through Complex Information Sharing

Signal acquisition via polarization modulation in single photon sources

Distributed compression-estimation using wireless sensor networks

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